I was named a Knight Fellow!
Resources
Who says we can't share?
This page will serve as a collection of resources for myself and others to use freely. Use the notes at your own risk, no promises that they're error-free or comprehensible to anyone but myself. 😀
Software
Some software I've helped develop.
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OSoMeTweet- A Python package for pulling data from Twitter's V2 endpoints. Designed with academics in mind.-
I gave a crash course demonstration of the package on Twitter's Twitch channel on September 3rd, 2021. You can check the notebook as an html file or download the actual notebook here.
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Social Media Stuff
Research that focuses on various aspects of social media. For the most part, misinformation, disinformation, social bots, bot detection, and similarly related topics.
- Bakshy, Messing, Adamic (2015) - Exposure to ideologically diverse news and opinion on Facebook
- Bollen, Mao, Zeng (2020) - Twitter mood predicts the stock market
- Bollenbacher, Pachecho, Hui, Ahn, Flaminni, Menczer (2021) - On the challenges of predicting microscopic dynamics of online conversations
- Creski (2020) - A Decade of Social Bot Detection
- Del Vicario, Bessi, Zollo, Petroni, Scala, Caldarelli, Stanley, and Quattrociocchi (2016) - The spreading of misinformation online
- Deutch (2020) - Tracking Facebook’s Election Misinformation "Super-Spreaders" (NewsGuard Special Report: Election Misinformation)
- Ferrara, Chang, Chen, Muric, Patel - Characterizing social media manipulation in the 2020 U.S. presidential election
- Ferrara, Varol, Davis, Menczer, Flammini (2016) - The rise of social bots
- Grinberg, Joseph, Friedland, Swire-Thompson, Lazer (2019) - Fake news on Twitter during the 2016 U.S. presidential election
- Kramer, Guillory, Hancock (2014) - Experimental evidence of massive-scale emotional contagion through social networks
- Memon & Carley (2020) - Characterizing COVID-19 Misinformation CommunitiesUsing a Novel Twitter Dataset
- Pei, Muchnik, Andrade Jr., Zheng, & Makse (2014) - Searching for superspreaders of information in real-world social media
- Shao, Ciampaglio, Varol, Yang, Flammini, Menczer (2018) - The spread of low-credibility content by social bots
- Vosoughi, Roy, Aral (2018) - The spread of true and false news online
Random Course Readings
These links are to notes on various literature encountered through my Ph.D. program at IU.
- Agar (2012) - Lively Science (Book, Ch. 1-2)
- Barabási and Albert (1999) - Emergence of Scaling in Random Networks
- Barthelemy (2014) - Scaling: lost in the smog
- Bernstein, Shore, Lazer (2018) - How intermittent breaks in interaction improve collective intelligence
- Bishop (2020) - How Scientists Can Stop Fooling Themselves Over Statistics
- Butts (2009) - Revisiting the Foundations of Network Analysis
- Christakis and Fowler (2010) - Social Network Sensors for Early Detection of Contagious Outbreaks
- Cristelli, Tacchela & Pietronero (2015) - The Heterogeneous Dynamics of Economic Complexity
- De Solla Price (1965) - Networks of Scientific Papers
- Domingos (2012) - A Few Useful Things to Know About Machine Learning
- Flake (1998) - The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation (pp. 1-8; 129-136)
- Helbing, Farkas, Vicsek (2000) - Simulating dynamical features of escape panic
- Kitsak, Gallos, Havlin, Liljeros, Muchnik, Stanley, Makse (2010) - Identification of influential spreaders in complex networks
- Kitchin (2014) - Big Data, new epistemologies andparadigm shifts
- Makse, Havlin & Stanley (1995) - Modeling Urban Growth Patterns
- Mantegna & Stanley (1995) - Scaling Behaviour in the Dynamics of an Economic Index
- Miller and Page (2009) - Complex Adaptive Systems: Computational Models of Social Life (Ch3: pp. 35-43)
- Miller and Page (2009) - Complex Adaptive Systems: Computational Models of Social Life (Ch5: pp. 57-77)
- McIntyre (2019) - The Scientific Attitude: Defending Science from Denial, Fraud, and Pseudoscience. (Intro & Ch. 1)
- Néda, Ravasz, Brecht, Vicsek & Barabási (2000) - The sound of many hands clapping
- Page - The Model Thinker: What You Need to Know to Make Data Work for You (Ch2: pp. 13-25)
- Radicchi, Fortunato, and Castellano (2008) - Universality of Citation Distributions: Toward an objective measure of scientific impact
- Salganik et al. (2020) - Measuring the predictability of life outcomes with a scientific mass collaboration
- Siever (1968) - Science: Observational, Experimental, Historical
- Song, Qu, Blumm, Barabasi (2010) - Limits of Predictability in Human Mobility
- Stanley et al. (1996) - Scaling behaviour in the growth of companies
- Tufte (2007) - The Visual Display of Quantitative Information, 2nd Edition (Chs. 4-6)
- Weaver (1948) - Science and Complexity
- Winsberg (2019) - Computer Simulations in Science
Complex Systems
This is a collection of single slide summaries that I (or others) presented for i709 Complex Systems on various topics.
You'll also find longer-form powerpoint presentations from when I was responsible for leading a presentation with another student.
- DeVerna (2020) - Economic Complexity (overview of a few papers)
- DeVerna (2020) - Generalized h-Index (Science of Science)
- DeVerna (2020) - Mobility Prediction and Travel Distance (Mobility)
- DeVerna (2020) - Modeling the Collective Motion of Escape Panic (Collective Motion)
- Aiyappa (2020) - Power Laws
- DeVerna (2020) - Scaling Laws and Mechanistic Insight (Science of Cities)
- DeVerna (2020) - Why Linear Regression and Power Law Distributions Don't Mix (Power Laws)
Funding
Some helpful links to student funding resources.
Misc.
Other random things that might be useful in the future.